An adaptive prediction approach for low power WSN

Description

In energy harvesting WSNs the prediction of the state of efficiency and energy budget allows the optimization of tasks scheduling based on the energy left or to be collected, e.g, which task will be executed based on its priority and the acquaintance of future available energy? Which suitable time is better to perform intensive computations? Could data be send imminently or is a delayed forwarding more efficient?

To overcome this questions, this thesis considers prediction methods to estimate system states. There are several existing mechanisms which vary in accuracy and complexity. However, as we are dealing with energy harvesting nodes the energy budget of nodes could vary throughout time and network. Therefore, an adaptive approach is desired to allow an effective and efficient prediction.

Job Description

The thesis aims to implement an efficient but accurate predictor for WSNs. In particular we consider undervolting capable nodes equipped with a thermal energy harvester. Thus, the predictive value is defined by the temperature and energy respectively. The most common method deployed in such similar cases is data smooting to extract the trend also called moving average. Firstly, existing algorithms like EWMA, WCMA, WCSMA, SEPAD, cloud cover forecast and also NN will be adapted to our use case and evaluated. Based on the previous tests, predictors will be classified according to their accuracy and computational cost. This will help to get an idea about the optimal choice in our case.

Temperature changes in a continuous way in time thus it is a univariate time series data. However, in practice the digital recording makes it discrete in time. When attempting to predict such data i.e univariate discrete time series, it is important to consider that large time series come along with high dimensionalities and noise along with trends. We can start by a general trend model based on time series variations:

Trend: long term change in the mean level.

Seasonal effect: seasonal variations.

Irregular fluctuations: after detrending and deseasoning, random fluctuations are left.

As the goal of this thesis is to develop a predictor based on temperature gradient in order to perform later, task scheduling and undervolting, a hybrid predictor might be considered which weights the overhead against the accuracy depending on the current energy budget. Datamining approaches are to be considered as well as the random rest to forecast following detrending.

After testing the predictors under R or matlab, we tend to implement them on the INGA node, which uses an Atmel 8 bit microcontroller and therefore represent a constraint in our case. Aiming for autonomous node, we don’t want our algorithm to be costy and large. However this comes with another constraint which is accuracy as most performant and efficient algorithms need a large memory and a high computational cost. Thus we are dealing with energy cost and accuracy duality. However, the predictor might rely on external data (e.g sink) from where it receives additional information already processed in the sink (e.g it needs more compuational power).

Vice versa the sink could also be used to lay out extensive calculations e.g. the training of an neural network.